Synchronization of Heterogeneous Vehicle Platoon Using Distributed PI Controller Designed Based on Cooperative Observer
Bibliographic record
Abstract
The challenge in designing distributed controllers for vehicle platoon synchronization arises when full-state information for control algorithm calculations cannot be obtained from the entire vehicle.Therefore, this paper presents a control scheme using a cooperative observer to estimate full-state information, enabling its use in calculating control signals.Instead of relying solely on a control signal proportional to the cooperative tracking error, the proposed control signal includes an additional integral form of the cooperative tracking error.This addition is expected to mitigate the effects of disturbances experienced by follower vehicles.Distributed control generally comprises two major components: The proportional-integral (PI) controller and the cooperative observer.The paper provides conditions for choosing control parameter values to guarantee the stability of the vehicle platoon.A numerical simulation of a vehicle platoon comprising one leader and ten followers is presented to demonstrate performance and validate the research results.Simulation results indicate that the controller performs effectively when followers experience constant disturbances, demonstrating the continuous achievement of vehicle platoon synchronization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".